

<!DOCTYPE html>
<!--[if IE 8]><html class="no-js lt-ie9" lang="en" > <![endif]-->
<!--[if gt IE 8]><!--> <html class="no-js" lang="en" > <!--<![endif]-->
<head>
  <meta charset="utf-8">
  
  <meta name="viewport" content="width=device-width, initial-scale=1.0">
  <meta name="Description" content="scikit-learn: machine learning in Python">

  
  <title>sklearn.cluster.k_means &mdash; scikit-learn 0.22 documentation</title>
  
  <link rel="canonical" href="http://scikit-learn.org/stable/modules/generated/sklearn.cluster.k_means.html" />

  
  <link rel="shortcut icon" href="../../_static/favicon.ico"/>
  

  <link rel="stylesheet" href="../../_static/css/vendor/bootstrap.min.css" type="text/css" />
  <link rel="stylesheet" href="../../_static/gallery.css" type="text/css" />
  <link rel="stylesheet" href="../../_static/css/theme.css" type="text/css" />
<script id="documentation_options" data-url_root="../../" src="../../_static/documentation_options.js"></script>
<script src="../../_static/jquery.js"></script> 
</head>
<body>
<nav id="navbar" class="sk-docs-navbar navbar navbar-expand-md navbar-light bg-light py-0">
  <div class="container-fluid sk-docs-container px-0">
      <a class="navbar-brand py-0" href="../../index.html">
        <img
          class="sk-brand-img"
          src="../../_static/scikit-learn-logo-small.png"
          alt="logo"/>
      </a>
    <button
      id="sk-navbar-toggler"
      class="navbar-toggler"
      type="button"
      data-toggle="collapse"
      data-target="#navbarSupportedContent"
      aria-controls="navbarSupportedContent"
      aria-expanded="false"
      aria-label="Toggle navigation"
    >
      <span class="navbar-toggler-icon"></span>
    </button>

    <div class="sk-navbar-collapse collapse navbar-collapse" id="navbarSupportedContent">
      <ul class="navbar-nav mr-auto">
        <li class="nav-item">
          <a class="sk-nav-link nav-link" href="../../install.html">Install</a>
        </li>
        <li class="nav-item">
          <a class="sk-nav-link nav-link" href="../../user_guide.html">User Guide</a>
        </li>
        <li class="nav-item">
          <a class="sk-nav-link nav-link" href="../classes.html">API</a>
        </li>
        <li class="nav-item">
          <a class="sk-nav-link nav-link" href="../../auto_examples/index.html">Examples</a>
        </li>
        <li class="nav-item">
          <a class="sk-nav-link nav-link nav-more-item-mobile-items" href="../../getting_started.html">Getting Started</a>
        </li>
        <li class="nav-item">
          <a class="sk-nav-link nav-link nav-more-item-mobile-items" href="../../tutorial/index.html">Tutorial</a>
        </li>
        <li class="nav-item">
          <a class="sk-nav-link nav-link nav-more-item-mobile-items" href="../../glossary.html">Glossary</a>
        </li>
        <li class="nav-item">
          <a class="sk-nav-link nav-link nav-more-item-mobile-items" href="../../developers/index.html">Development</a>
        </li>
        <li class="nav-item">
          <a class="sk-nav-link nav-link nav-more-item-mobile-items" href="../../faq.html">FAQ</a>
        </li>
        <li class="nav-item">
          <a class="sk-nav-link nav-link nav-more-item-mobile-items" href="../../related_projects.html">Related packages</a>
        </li>
        <li class="nav-item">
          <a class="sk-nav-link nav-link nav-more-item-mobile-items" href="../../roadmap.html">Roadmap</a>
        </li>
        <li class="nav-item">
          <a class="sk-nav-link nav-link nav-more-item-mobile-items" href="../../about.html">About us</a>
        </li>
        <li class="nav-item">
          <a class="sk-nav-link nav-link nav-more-item-mobile-items" href="https://github.com/scikit-learn/scikit-learn">GitHub</a>
        </li>
        <li class="nav-item">
          <a class="sk-nav-link nav-link nav-more-item-mobile-items" href="https://scikit-learn.org/dev/versions.html">Other Versions</a>
        </li>
        <li class="nav-item dropdown nav-more-item-dropdown">
          <a class="sk-nav-link nav-link dropdown-toggle" href="#" id="navbarDropdown" role="button" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false">More</a>
          <div class="dropdown-menu" aria-labelledby="navbarDropdown">
              <a class="sk-nav-dropdown-item dropdown-item" href="../../getting_started.html">Getting Started</a>
              <a class="sk-nav-dropdown-item dropdown-item" href="../../tutorial/index.html">Tutorial</a>
              <a class="sk-nav-dropdown-item dropdown-item" href="../../glossary.html">Glossary</a>
              <a class="sk-nav-dropdown-item dropdown-item" href="../../developers/index.html">Development</a>
              <a class="sk-nav-dropdown-item dropdown-item" href="../../faq.html">FAQ</a>
              <a class="sk-nav-dropdown-item dropdown-item" href="../../related_projects.html">Related packages</a>
              <a class="sk-nav-dropdown-item dropdown-item" href="../../roadmap.html">Roadmap</a>
              <a class="sk-nav-dropdown-item dropdown-item" href="../../about.html">About us</a>
              <a class="sk-nav-dropdown-item dropdown-item" href="https://github.com/scikit-learn/scikit-learn">GitHub</a>
              <a class="sk-nav-dropdown-item dropdown-item" href="https://scikit-learn.org/dev/versions.html">Other Versions</a>
          </div>
        </li>
      </ul>
      <div id="searchbox" role="search">
          <div class="searchformwrapper">
          <form class="search" action="../../search.html" method="get">
            <input class="sk-search-text-input" type="text" name="q" aria-labelledby="searchlabel" />
            <input class="sk-search-text-btn" type="submit" value="Go" />
          </form>
          </div>
      </div>
    </div>
  </div>
</nav>
<div class="d-flex" id="sk-doc-wrapper">
    <input type="checkbox" name="sk-toggle-checkbox" id="sk-toggle-checkbox">
    <label id="sk-sidemenu-toggle" class="sk-btn-toggle-toc btn sk-btn-primary" for="sk-toggle-checkbox">Toggle Menu</label>
    <div id="sk-sidebar-wrapper" class="border-right">
      <div class="sk-sidebar-toc-wrapper">
        <div class="sk-sidebar-toc-logo">
          <a href="../../index.html">
            <img
              class="sk-brand-img"
              src="../../_static/scikit-learn-logo-small.png"
              alt="logo"/>
          </a>
        </div>
        <div class="btn-group w-100 mb-2" role="group" aria-label="rellinks">
            <a href="sklearn.cluster.estimate_bandwidth.html" role="button" class="btn sk-btn-rellink py-1" sk-rellink-tooltip="sklearn.cluster.estimate_bandwidth">Prev</a><a href="../classes.html" role="button" class="btn sk-btn-rellink py-1" sk-rellink-tooltip="API Reference">Up</a>
            <a href="sklearn.cluster.mean_shift.html" role="button" class="btn sk-btn-rellink py-1" sk-rellink-tooltip="sklearn.cluster.mean_shift">Next</a>
        </div>
        <div class="alert alert-danger p-1 mb-2" role="alert">
          <p class="text-center mb-0">
          <strong>scikit-learn 0.22</strong><br/>
          <a href="http://scikit-learn.org/dev/versions.html">Other versions</a>
          </p>
        </div>
        <div class="alert alert-warning p-1 mb-2" role="alert">
          <p class="text-center mb-0">
            Please <a class="font-weight-bold" href="../../about.html#citing-scikit-learn"><string>cite us</string></a> if you use the software.
          </p>
        </div>
          <div class="sk-sidebar-toc">
            <ul>
<li><a class="reference internal" href="#"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.cluster</span></code>.k_means</a></li>
</ul>

          </div>
      </div>
    </div>
    <div id="sk-page-content-wrapper">
      <div class="sk-page-content container-fluid body px-md-3" role="main">
        
  <div class="section" id="sklearn-cluster-k-means">
<h1><a class="reference internal" href="../classes.html#module-sklearn.cluster" title="sklearn.cluster"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.cluster</span></code></a>.k_means<a class="headerlink" href="#sklearn-cluster-k-means" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="sklearn.cluster.k_means">
<code class="sig-prename descclassname">sklearn.cluster.</code><code class="sig-name descname">k_means</code><span class="sig-paren">(</span><em class="sig-param">X</em>, <em class="sig-param">n_clusters</em>, <em class="sig-param">sample_weight=None</em>, <em class="sig-param">init='k-means++'</em>, <em class="sig-param">precompute_distances='auto'</em>, <em class="sig-param">n_init=10</em>, <em class="sig-param">max_iter=300</em>, <em class="sig-param">verbose=False</em>, <em class="sig-param">tol=0.0001</em>, <em class="sig-param">random_state=None</em>, <em class="sig-param">copy_x=True</em>, <em class="sig-param">n_jobs=None</em>, <em class="sig-param">algorithm='auto'</em>, <em class="sig-param">return_n_iter=False</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/cluster/_k_means.py#L178"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.k_means" title="Permalink to this definition">¶</a></dt>
<dd><p>K-means clustering algorithm.</p>
<p>Read more in the <a class="reference internal" href="../clustering.html#k-means"><span class="std std-ref">User Guide</span></a>.</p>
<dl class="field-list">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl>
<dt><strong>X</strong><span class="classifier">array-like or sparse matrix, shape (n_samples, n_features)</span></dt><dd><p>The observations to cluster. It must be noted that the data
will be converted to C ordering, which will cause a memory copy
if the given data is not C-contiguous.</p>
</dd>
<dt><strong>n_clusters</strong><span class="classifier">int</span></dt><dd><p>The number of clusters to form as well as the number of
centroids to generate.</p>
</dd>
<dt><strong>sample_weight</strong><span class="classifier">array-like, shape (n_samples,), optional</span></dt><dd><p>The weights for each observation in X. If None, all observations
are assigned equal weight (default: None)</p>
</dd>
<dt><strong>init</strong><span class="classifier">{‘k-means++’, ‘random’, or ndarray, or a callable}, optional</span></dt><dd><p>Method for initialization, default to ‘k-means++’:</p>
<p>‘k-means++’ : selects initial cluster centers for k-mean
clustering in a smart way to speed up convergence. See section
Notes in k_init for more details.</p>
<p>‘random’: choose k observations (rows) at random from data for
the initial centroids.</p>
<p>If an ndarray is passed, it should be of shape (n_clusters, n_features)
and gives the initial centers.</p>
<p>If a callable is passed, it should take arguments X, k and
and a random state and return an initialization.</p>
</dd>
<dt><strong>precompute_distances</strong><span class="classifier">{‘auto’, True, False}</span></dt><dd><p>Precompute distances (faster but takes more memory).</p>
<p>‘auto’ : do not precompute distances if n_samples * n_clusters &gt; 12
million. This corresponds to about 100MB overhead per job using
double precision.</p>
<p>True : always precompute distances</p>
<p>False : never precompute distances</p>
</dd>
<dt><strong>n_init</strong><span class="classifier">int, optional, default: 10</span></dt><dd><p>Number of time the k-means algorithm will be run with different
centroid seeds. The final results will be the best output of
n_init consecutive runs in terms of inertia.</p>
</dd>
<dt><strong>max_iter</strong><span class="classifier">int, optional, default 300</span></dt><dd><p>Maximum number of iterations of the k-means algorithm to run.</p>
</dd>
<dt><strong>verbose</strong><span class="classifier">boolean, optional</span></dt><dd><p>Verbosity mode.</p>
</dd>
<dt><strong>tol</strong><span class="classifier">float, optional</span></dt><dd><p>The relative increment in the results before declaring convergence.</p>
</dd>
<dt><strong>random_state</strong><span class="classifier">int, RandomState instance or None (default)</span></dt><dd><p>Determines random number generation for centroid initialization. Use
an int to make the randomness deterministic.
See <a class="reference internal" href="../../glossary.html#term-random-state"><span class="xref std std-term">Glossary</span></a>.</p>
</dd>
<dt><strong>copy_x</strong><span class="classifier">bool, optional</span></dt><dd><p>When pre-computing distances it is more numerically accurate to center
the data first.  If copy_x is True (default), then the original data is
not modified, ensuring X is C-contiguous.  If False, the original data
is modified, and put back before the function returns, but small
numerical differences may be introduced by subtracting and then adding
the data mean, in this case it will also not ensure that data is
C-contiguous which may cause a significant slowdown.</p>
</dd>
<dt><strong>n_jobs</strong><span class="classifier">int or None, optional (default=None)</span></dt><dd><p>The number of jobs to use for the computation. This works by computing
each of the n_init runs in parallel.</p>
<p><code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/parallel.html#joblib.parallel_backend" title="(in joblib v0.14.1.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
<code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="../../glossary.html#term-n-jobs"><span class="xref std std-term">Glossary</span></a>
for more details.</p>
</dd>
<dt><strong>algorithm</strong><span class="classifier">“auto”, “full” or “elkan”, default=”auto”</span></dt><dd><p>K-means algorithm to use. The classical EM-style algorithm is “full”.
The “elkan” variation is more efficient by using the triangle
inequality, but currently doesn’t support sparse data. “auto” chooses
“elkan” for dense data and “full” for sparse data.</p>
</dd>
<dt><strong>return_n_iter</strong><span class="classifier">bool, optional</span></dt><dd><p>Whether or not to return the number of iterations.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>centroid</strong><span class="classifier">float ndarray with shape (k, n_features)</span></dt><dd><p>Centroids found at the last iteration of k-means.</p>
</dd>
<dt><strong>label</strong><span class="classifier">integer ndarray with shape (n_samples,)</span></dt><dd><p>label[i] is the code or index of the centroid the
i’th observation is closest to.</p>
</dd>
<dt><strong>inertia</strong><span class="classifier">float</span></dt><dd><p>The final value of the inertia criterion (sum of squared distances to
the closest centroid for all observations in the training set).</p>
</dd>
<dt><strong>best_n_iter</strong><span class="classifier">int</span></dt><dd><p>Number of iterations corresponding to the best results.
Returned only if <code class="docutils literal notranslate"><span class="pre">return_n_iter</span></code> is set to True.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<div class="clearer"></div></div>


      </div>
    <div class="container">
      <footer class="sk-content-footer">
            &copy; 2007 - 2019, scikit-learn developers (BSD License).
          <a href="../../_sources/modules/generated/sklearn.cluster.k_means.rst.txt" rel="nofollow">Show this page source</a>
      </footer>
    </div>
  </div>
</div>
<script src="../../_static/js/vendor/bootstrap.min.js"></script>

<script>
    window.ga=window.ga||function(){(ga.q=ga.q||[]).push(arguments)};ga.l=+new Date;
    ga('create', 'UA-22606712-2', 'auto');
    ga('set', 'anonymizeIp', true);
    ga('send', 'pageview');
</script>
<script async src='https://www.google-analytics.com/analytics.js'></script>


<script>
$(document).ready(function() {
    /* Add a [>>>] button on the top-right corner of code samples to hide
     * the >>> and ... prompts and the output and thus make the code
     * copyable. */
    var div = $('.highlight-python .highlight,' +
                '.highlight-python3 .highlight,' +
                '.highlight-pycon .highlight,' +
		'.highlight-default .highlight')
    var pre = div.find('pre');

    // get the styles from the current theme
    pre.parent().parent().css('position', 'relative');
    var hide_text = 'Hide prompts and outputs';
    var show_text = 'Show prompts and outputs';

    // create and add the button to all the code blocks that contain >>>
    div.each(function(index) {
        var jthis = $(this);
        if (jthis.find('.gp').length > 0) {
            var button = $('<span class="copybutton">&gt;&gt;&gt;</span>');
            button.attr('title', hide_text);
            button.data('hidden', 'false');
            jthis.prepend(button);
        }
        // tracebacks (.gt) contain bare text elements that need to be
        // wrapped in a span to work with .nextUntil() (see later)
        jthis.find('pre:has(.gt)').contents().filter(function() {
            return ((this.nodeType == 3) && (this.data.trim().length > 0));
        }).wrap('<span>');
    });

    // define the behavior of the button when it's clicked
    $('.copybutton').click(function(e){
        e.preventDefault();
        var button = $(this);
        if (button.data('hidden') === 'false') {
            // hide the code output
            button.parent().find('.go, .gp, .gt').hide();
            button.next('pre').find('.gt').nextUntil('.gp, .go').css('visibility', 'hidden');
            button.css('text-decoration', 'line-through');
            button.attr('title', show_text);
            button.data('hidden', 'true');
        } else {
            // show the code output
            button.parent().find('.go, .gp, .gt').show();
            button.next('pre').find('.gt').nextUntil('.gp, .go').css('visibility', 'visible');
            button.css('text-decoration', 'none');
            button.attr('title', hide_text);
            button.data('hidden', 'false');
        }
    });

	/*** Add permalink buttons next to glossary terms ***/
	$('dl.glossary > dt[id]').append(function() {
		return ('<a class="headerlink" href="#' +
			    this.getAttribute('id') +
			    '" title="Permalink to this term">¶</a>');
	});
  /*** Hide navbar when scrolling down ***/
  // Returns true when headerlink target matches hash in url
  (function() {
    hashTargetOnTop = function() {
        var hash = window.location.hash;
        if ( hash.length < 2 ) { return false; }

        var target = document.getElementById( hash.slice(1) );
        if ( target === null ) { return false; }

        var top = target.getBoundingClientRect().top;
        return (top < 2) && (top > -2);
    };

    // Hide navbar on load if hash target is on top
    var navBar = document.getElementById("navbar");
    var navBarToggler = document.getElementById("sk-navbar-toggler");
    var navBarHeightHidden = "-" + navBar.getBoundingClientRect().height + "px";
    var $window = $(window);

    hideNavBar = function() {
        navBar.style.top = navBarHeightHidden;
    };

    showNavBar = function() {
        navBar.style.top = "0";
    }

    if (hashTargetOnTop()) {
        hideNavBar()
    }

    var prevScrollpos = window.pageYOffset;
    hideOnScroll = function(lastScrollTop) {
        if (($window.width() < 768) && (navBarToggler.getAttribute("aria-expanded") === 'true')) {
            return;
        }
        if (lastScrollTop > 2 && (prevScrollpos <= lastScrollTop) || hashTargetOnTop()){
            hideNavBar()
        } else {
            showNavBar()
        }
        prevScrollpos = lastScrollTop;
    };

    /*** high preformance scroll event listener***/
    var raf = window.requestAnimationFrame ||
        window.webkitRequestAnimationFrame ||
        window.mozRequestAnimationFrame ||
        window.msRequestAnimationFrame ||
        window.oRequestAnimationFrame;
    var lastScrollTop = $window.scrollTop();

    if (raf) {
        loop();
    }

    function loop() {
        var scrollTop = $window.scrollTop();
        if (lastScrollTop === scrollTop) {
            raf(loop);
            return;
        } else {
            lastScrollTop = scrollTop;
            hideOnScroll(lastScrollTop);
            raf(loop);
        }
    }
  })();
});

</script>
    
<script id="MathJax-script" async src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-chtml.js"></script>
    
</body>
</html>